R convert zipcode or lat/long to county

A simple option is to use the geocode() function in ggmap, with the option output="more" or output="all.

This can take flexible input, such as the address or lat/lon, and returns Address, city, county, state, country, postal code, etc, as a list.

require("ggmap")
address <- geocode("Yankee Stadium", output="more")

str(address)
$ lon                        : num -73.9
$ lat                        : num 40.8
$ type                       : Factor w/ 1 level "stadium": 1
$ loctype                    : Factor w/ 1 level "approximate": 1
$ address                    : Factor w/ 1 level "yankee stadium, 1 east 161st street, bronx, ny 10451, usa": 1
$ north                      : num 40.8
$ south                      : num 40.8
$ east                       : num -73.9
$ west                       : num -73.9
$ postal_code                : chr "10451"
$ country                    : chr "united states"
$ administrative_area_level_2: chr "bronx"
$ administrative_area_level_1: chr "ny"
$ locality                   : chr "new york"
$ street                     : chr "east 161st street"
$ streetNo                   : num 1
$ point_of_interest          : chr "yankee stadium"
$ query                      : chr "Yankee Stadium"

Another solution is to use a census shapefile, and the same over() command from the question. I ran into a problem using the maptools base map: because it uses the WGS84 datum, in North America, points that were within a few miles of the coast were mapped incorrectly and about 5% of my data set did not match up.

try this, using the sp package and Census TIGERLine shape files

counties <- readShapeSpatial("maps/tl_2013_us_county.shp", proj4string=CRS("+proj=longlat +datum=NAD83"))

# Convert pointsDF to a SpatialPoints object 
pointsSP <- SpatialPoints(pointsDF, proj4string=CRS("+proj=longlat +datum=NAD83"))

countynames <- over(pointsSP, counties)
countynames <- countynames$NAMELSAD

I ended up using the suggestion from JoshO'Brien mentioned above and found here.

I took his code and changed state to county as shown here:

library(sp)
library(maps)
library(maptools)

# The single argument to this function, pointsDF, is a data.frame in which:
#   - column 1 contains the longitude in degrees (negative in the US)
#   - column 2 contains the latitude in degrees

latlong2county <- function(pointsDF) {
    # Prepare SpatialPolygons object with one SpatialPolygon
    # per county
    counties <- map('county', fill=TRUE, col="transparent", plot=FALSE)
    IDs <- sapply(strsplit(counties$names, ":"), function(x) x[1])
    counties_sp <- map2SpatialPolygons(counties, IDs=IDs,
                     proj4string=CRS("+proj=longlat +datum=WGS84"))

    # Convert pointsDF to a SpatialPoints object 
    pointsSP <- SpatialPoints(pointsDF, 
                    proj4string=CRS("+proj=longlat +datum=WGS84"))

    # Use 'over' to get _indices_ of the Polygons object containing each point 
    indices <- over(pointsSP, counties_sp)

    # Return the county names of the Polygons object containing each point
    countyNames <- sapply(counties_sp@polygons, function(x) x@ID)
    countyNames[indices]
}

# Test the function using points in Wisconsin and Oregon.
testPoints <- data.frame(x = c(-90, -120), y = c(44, 44))

latlong2county(testPoints)
[1] "wisconsin,juneau" "oregon,crook" # IT WORKS

I think the package "noncensus" is helpful.

corresponding is what I use to match zipcode with county

### code for get county based on zipcode

library(noncensus)
data(zip_codes)
data(counties)

state_fips  = as.numeric(as.character(counties$state_fips))
county_fips = as.numeric(as.character(counties$county_fips))    
counties$fips = state_fips*1000+county_fips    
zip_codes$fips =  as.numeric(as.character(zip_codes$fips))

# test
temp = subset(zip_codes, zip == "30329")    
subset(counties, fips == temp$fips)

Matching Zipcodes to Counties is difficult. (Certain zip codes span more than one county and sometimes more than one state. For example 30165)

I am not aware of any specific R package that can match these up for you.

However, you can get a nice table from the Missouri Census Data Center.
You can use the following for data extraction: http://bit.ly/S63LNU

A sample output might look like:

    state,zcta5,ZIPName,County,County2
    01,30165,"Rome, GA",Cherokee AL,
    01,31905,"Fort Benning, GA",Russell AL,
    01,35004,"Moody, AL",St. Clair AL,
    01,35005,"Adamsville, AL",Jefferson AL,
    01,35006,"Adger, AL",Jefferson AL,Walker AL
    ...

Note the County2. metadata explanation can be found here.

    county 
    The county in which the ZCTA is all or mostly contained. Over 90% of ZCTAs fall entirely within a single county.

    county2 
    The "secondary" county for the ZCTA, i.e. the county which has the 2nd largest intersection with it. Over 90% of the time this value will be blank.

See also ANSI County codes http://www.census.gov/geo/www/ansi/ansi.html